Apparatus and methods for generating an instruction set for a user

ABSTRACT

An apparatus and method for generating an instruction set for a user is provided. The apparatus includes at least a processor and a memory connected to the processor. The memory contains instructions configuring the at least a processor to receive a client datum from a client, where the client datum describes resources of the client, and to receive a user datum from the user, where user datum includes a target datum that describes resource transfer data from the client to the user. Initiation of resource transfer described by the target datum is triggered by the pattern exceeding a threshold. In addition, the memory contains instructions configuring the at least a processor to generate an interface query data structure including an input field and to display the first transfer datum and the second transfer datum hierarchically based on a user-input datum input to the input field.

FIELD OF THE INVENTION

The present invention generally relates to the field of resourcemanagement regarding timely repayment for services rendered. Inparticular, the present invention is directed to an apparatus andmethods for data processing for generating an instruction set for auser.

BACKGROUND

Current data processing or digital resource management techniques tendto focus on general behavior descriptions, rather classifying clientrepayment behavior categorized into multiple categories and furtherdefined by a triggering event. Prior programmatic attempts to resolvethese and other related issues have suffered from inadequateuser-provided data intake and subsequent processing capabilities.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for generating an instruction set for a useris provided. The apparatus includes at least a processor. A memory isconnected to the processor. The memory contains instructions configuringthe at least a processor to receive a client datum from a client. Theclient datum describes resources of the client and a pattern that isrepresentative of client interaction with the user. In addition, thememory contains instructions configuring the at least a processor toreceive a user datum from the user. The user datum includes a targetdatum that describes resource transfer data from the client to the user.Initiation of resource transfer described by the target datum istriggered by the pattern exceeding a threshold. In addition, the memorycontains instructions configuring the at least a processor to classifythe client datum and the user datum to a plurality of categories,calculate the target datum based on classification of the client datumand the user datum to the plurality of categories, and identify a firsttransfer datum and at least a second transfer datum from transfer data.Refining at least the first transfer datum includes classifying at leastthe first transfer datum to the target datum and ranking the firsttransfer datum and the second transfer datum relative to the targetdatum. In addition, the memory contains instructions configuring the atleast a processor to generate an interface query data structureincluding an input field based on ranking the first transfer datum andthe second transfer datum. More particularly, the interface query datastructure configures a remote display device to display the input fieldto the user and receive at least a user-input datum into the inputfield. The user-input datum describes data for selecting a preferredattribute of transfer data associated with one or more instances ofrankings of the first transfer datum and the second transfer datum.Still further, the interface query data structure configures a remotedisplay device to display the instruction set including displaying thefirst transfer datum and at least the second transfer datumhierarchically based on the user-input datum.

In another aspect, a method for generating an instruction set for a useris provided. The method includes receiving, by a computing device, aclient datum from a client. The client datum describes resources of theclient and a pattern that is representative of client interaction withthe user. The method includes receiving, by the computing device, a userdatum from the user, the user datum including a target datum thatdescribes resource transfer data from the client to the user. Initiationof resource transfer described by the target datum is triggered by thepattern exceeding a threshold. The method includes classifying, by thecomputing device, the client datum, and the user datum to a plurality ofcategories. The method includes calculating, by the computing device,the target datum based on classification of the client datum and theuser datum to the plurality of categories. The method includesidentifying, by the computing device, a first transfer datum and atleast a second transfer datum from transfer data. Refining at least thefirst transfer datum includes classifying, by the computing device, atleast the first transfer datum to the target datum and ranking, by thecomputing device, the first transfer datum and the second transfer datumrelative to the target datum. The method includes generating, by thecomputing device, an interface query data structure including an inputfield based on ranking the first transfer datum and the second transferdatum. More particularly, the interface query data structure configuresa remote display device to display the input field to the user, receiveat least a user-input datum into the input field. The user-input datumdescribes data for selecting a preferred attribute of transfer dataassociated with one or more instances of rankings of the first transferdatum and the second transfer datum. In addition, the interface querydata structure configures a remote display device to display theinstruction set including displaying the first transfer datum and atleast the second transfer datum hierarchically based on the user-inputdatum.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an embodiment of an apparatus forgenerating an instruction set for a user;

FIGS. 2A-2B are diagrammatic representations of multiple exemplaryembodiments of output generated by an interface query data structure;

FIG. 3 is a diagrammatic representation of a transfer objectivedatabase;

FIG. 4 is a block diagram of exemplary machine-learning processes;

FIG. 5 is a graph illustrating an exemplary relationship between fuzzysets;

FIG. 6 is a flow diagram of an exemplary method for generating aninstruction set for a user;

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to anapparatus and methods for generating an instruction set for a user.Described processes are executed by a computing device including aprocessor, which is configured to execute any one or more of thedescribed steps. A memory is connected to the processor and containsinstructions configuring the processor to receive a client datum from aclient. The client datum describes resources of the client and a patternthat is representative of client interaction with the user. In addition,the memory contains instructions configuring the at least a processor toreceive a user datum from the user. The user datum includes a targetdatum that describes resource transfer data from the client to the user.Initiation of resource transfer described by the target datum istriggered by the pattern exceeding a threshold. In addition, the memorycontains instructions configuring the at least a processor to classifythe client datum and the user datum to a plurality of categories,calculate the target datum based on classification of the client datumand the user datum to the plurality of categories, and identify a firsttransfer datum and at least a second transfer datum from transfer data.Refining at least the first transfer datum includes classifying at leastthe first transfer datum to the target datum and ranking the firsttransfer datum and the second transfer datum relative to the targetdatum.

In addition, the memory contains instructions configuring the at least aprocessor to generate an “interface query data structure” including aninput field based on ranking the first transfer datum and the secondtransfer datum. An “interface query data structure,” as used in thisdisclosure, is an example of data structure used to “query,” such as bydigitally requesting, for data results from a database and/or for actionon the data. “Data structure,” in the field of computer science, is adata organization, management, and storage format that is usually chosenfor efficient access to data. More particularly, a “data structure” is acollection of data values, the relationships among them, and thefunctions or operations that can be applied to the data. Data structuresalso provide a means to manage relatively large amounts of dataefficiently for uses such as large databases and internet indexingservices. Generally, efficient data structures are essential todesigning efficient algorithms. Some formal design methods andprogramming languages emphasize data structures, rather than algorithms,as an essential organizing factor in software design. In addition, datastructures can be used to organize the storage and retrieval ofinformation stored in, for example, both main memory and secondarymemory.

Therefore, “interface query data structure,” as used herein, refers to,for example, a data organization format used to digitally request a dataresult or action on the data. In addition, the “interface query datastructure” can be displayed on a display device, such as a digitalperipheral, smartphone, or other similar device, etc. The interfacequery data structure may be generated based on received “user data,”defined as including historical data of the user. Historical data mayinclude attributes and facts about a user that are already publiclyknown or otherwise available, such as quarterly earnings for publiclytraded businesses, or health and/or personal training specifics in thecontext of physical performance training, etc. In some embodiments,interface query data structure prompts may be generated by amachine-learning model. As a non-limiting example, the machine-learningmodel may receive user data and output interface query data structurequestions.

Accordingly, as used herein, the interface query data structureconfigures a remote display device to display the input field to theuser and receive at least a user-input datum into the input field. Theuser-input datum describes data for selecting a preferred attribute oftransfer data associated with one or more instances of rankings of thefirst transfer datum and the second transfer datum. Still further, theinterface query data structure configures a remote display device todisplay the instruction set including displaying the first transferdatum and at least the second transfer datum hierarchically based on theuser-input datum.

Referring now to FIG. 1 , an exemplary embodiment of apparatus 100A forproviding a customized skill factor datum to a user. In one or moreembodiments, apparatus 100 includes computing device 104, which mayinclude without limitation a microcontroller, microprocessor (alsoreferred to in this disclosure as a “processor”), digital signalprocessor (DSP) and/or system on a chip (SoC) as described in thisdisclosure. Computing device 104 may include a computer system with oneor more processors (e.g., CPUs), a graphics processing unit (GPU), orany combination thereof. Computing device 104 may include a memorycomponent, such as memory component 140, which may include a memory,such as a main memory and/or a static memory, as discussed further inthis disclosure below. Computing device 104 may include a displaycomponent (e.g., display device 132, which may be positioned remotelyrelative to computing device 104), as discussed further below in thedisclosure. In one or more embodiments, computing device 104 mayinclude, be included in, and/or communicate with a mobile device such asa mobile telephone or smartphone. Computing device 104 may include asingle computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. Computingdevice 104 may interface or communicate with one or more additionaldevices, as described below in further detail, via a network interfacedevice. Network interface device may be utilized for connectingcomputing device 104 to one or more of a variety of networks, and one ormore devices. Examples of a network interface device include, but arenot limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, any combination thereof, and thelike. Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, acampus, or other relatively small geographic space), a telephonenetwork, a data network associated with a telephone/voice provider(e.g., a mobile communications provider data and/or voice network), adirect connection between two computing devices, and any combinationsthereof. A network may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software etc.) may be communicated to and/or from acomputer and/or a computing device. Computing device 104 may include butis not limited to, for example, a computing device or cluster ofcomputing devices in a first location and a second computing device orcluster of computing devices in a second location. Computing device 104may include one or more computing devices dedicated to data storage,security, distribution of traffic for load balancing, and the like.Computing device 104 may distribute one or more computing tasks, asdescribed below, across a plurality of computing devices of computingdevice 104, which may operate in parallel, in series, redundantly, or inany other manner used for distribution of tasks or memory betweencomputing devices. Computing device 104 may be implemented using a“shared nothing” architecture in which data is cached at the worker, inan embodiment, this may enable scalability of apparatus 100 and/orcomputing device 104.

With continued reference to FIG. 1 , computing device 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, computingdevice 104A may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Computing device 104 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

With continued reference to FIG. 1 , computing device 104 is configuredto receive at least an element of client datum 108, which may includedata describing current preferences relating to achieving a target bythe user. For the purpose of this disclosure, a “client datum” is anelement, datum, or elements of data client information, payment, and/orthe like. Accordingly, the client datum may describe various resources(e.g., monetary, land, intellectual property, and other forms ofintangible assets and the like) of the client and a pattern that isrepresentative of client interaction with the user (as introducedearlier). In some embodiments, client datum 108 may be input intocomputing device 104 manually by the client, who may be associated withany type or form of establishment (e.g., a business, university,non-profit, charity, etc.), or may be an independent entity (e.g., asolo proprietor, an athlete, an artist, etc.). In some instances, clientdatum 108 may be extracted from a business profile, such as that may beavailable via the Internet on LinkedIn®, a business andemployment-focused social media platform that works through websites andmobile apps owned my Microsoft® Corp., of Redmond, WA). Moreparticularly, such a business profile may include the past achievementsof a user in various fields such as business, finance, and personal,depending on one or more particular related circumstances. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various other ways or situations in which client datum 108may be input, generated, or extracted for various situations and goals.For example, in an example where the client is a business, client datum108 may be extracted from or otherwise be based on the client's businessprofile, which may include various business records such as financialrecords, inventory record, sales records, and the like. In addition, inone or more embodiments, client datum 108 may be generated by evaluatinginteractions with external entities, such as third parties. Moreparticularly, in a business-related context, such an example externalentity (or third party) may be that offered by Moody's InvestorsServices, Inc., Moody's Analytics, Inc. and/or their respectiveaffiliates and licensors, of New York, NY. Services rendered may includeproviding international financial research on bonds issued by commercialand government entities, including ranking the creditworthiness ofborrowers using a standardized ratings scale which measures expectedinvestor loss in the event of default. In such an example, client datum108 extracted from such an external entity may include ratings for debtsecurities in several bond market segments, including government,municipal and corporate bonds, as well as various managed investmentssuch as money market funds and fixed-income funds and financialinstitutions including banks and non-bank finance companies and assetclasses in structured finance.

In addition, or the alternative, in one or more embodiments, clientdatum 108 may be acquired using web trackers or data scrapers. As usedherein, “web trackers” are scripts (e.g., programs or sequences ofinstructions that are interpreted or carried out by another programrather than by a computer) on websites designed to derive data pointsabout user preferences and identify. In some embodiments, such webtrackers may track activity of the user on the Internet. Also, as usedherein, “data scrapers” are computer programs that extract data fromhuman-readable output coming from another program. For example, datascrapers may be programmed to gather data on user from user's socialmedia profiles, personal websites, and the like. In some embodiments,client datum 108 may be numerically quantified (e.g., by data describingdiscrete real integer values, such as 1, 2, 3 . . . n, where n=auser-defined or prior programmed maximum value entry, such as 10, wherelower values denote lesser significance relating to favorable businessoperation and higher values denote greater significance relating tofavorable business operation). For example, for classifying at least anelement describing a pattern of client datum 108 (e.g., of a business)to target datum 118 in the context of fiscal integrity in financialservices and retirement planning, client datum 108 may equal “3” for abusiness, such as an investment bank stock or mutual fund share, etc.,suffering from credit liquidity problems stemming from a rapidlydeteriorating macroeconomic environment and/or poor quality seniormanagement, a “5” for only matching industry peers, and an “8” forsignificantly outperforming industry peers, etc.

Other example values are possible along with other exemplary attributesand facts about a client (e.g., a business entity, or an aspiringathlete) that are already known and may be tailored to a particularsituation where explicit business guidance (e.g., provided by thedescribed progression sequence) is sought. In one or more alternativeembodiments, client datum 108 may be described by data organized in orrepresented by lattices, grids, vectors, etc., and may be adjusted orselected as necessary to accommodate particular client-definedcircumstances or any other format or structure for use as a calculativevalue that a person skilled in the art would recognize as suitable uponreview of the entirety of this disclosure.

In one or more embodiments, client datum 108 may be provided to orreceived by computing device 104 using various means. In one or moreembodiments, client datum 108 may be provided to computing device 104 bya business, such as by a human authorized to act on behalf of thebusiness including any type of executive officer, an authorized dataentry specialist or other type of related professional, or otherauthorized person or digital entity (e.g., software packagecommunicatively coupled with a database storing relevant information)that is interested in improving and/or optimizing performance of thebusiness overall, or in a particular area or field over a definedduration, such as a quarter or six months. In some examples, a human maymanually enter client datum 108 into computing device 104 using, forexample, user input field 148 of graphical user interface (GUI) 136 ofdisplay device 132. For example, and without limitation, a human may usedisplay device 132 to navigate the GUI 136 and provide client datum 108to computing device 104. Non-limiting exemplary input devices includekeyboards, joy sticks, light pens, tracker balls, scanners, tablet,microphones, mouses, switches, buttons, sliders, touchscreens, and thelike. In other embodiments, client datum 108 may be provided tocomputing device 104 by a database over a network from, for example, anetwork-based platform. Client datum 108 may be stored, in one or moreembodiments, in database 150 and communicated to computing device 104upon a retrieval request from a human and/or other digital device (notshown in FIG. 1 ) communicatively connected with computing device 104.In other embodiments, client datum 108 may be communicated from athird-party application, such as from a third-party application on athird-party server, using a network. For example, client datum 108 maybe downloaded from a hosting website for a particular area, such as anetworking group for small business owners in a certain city, or for aplanning group for developing new products to meet changing clientexpectations, or for performance improvement relating to increasingbusiness throughput volume and profit margins for any type of business,ranging from smaller start-ups to larger organizations that arefunctioning enterprises. In one or more embodiments, computing device104 may extract client datum 108 from an accumulation of informationprovided by database 150. For instance, and without limitation,computing device 104 may extract needed information database 150regarding improvement in a particular area sought-after by the businessand avoid taking any information determined to be unnecessary. This maybe performed by computing device 104 using a machine-learning model,which is described in this disclosure further below.

At a high level, and as used herein, “machine-learning” describes afield of inquiry devoted to understanding and building methods that“learn”—that is, methods that leverage data to improve performance onsome set of defined tasks. Machine-learning algorithms may build amachine-learning model based on sample data, known as “training data”,to make predictions or decisions without being explicitly programmed todo so. Such algorithms may function by making data-driven predictions ordecisions by building a mathematical model from input data. These inputdata used to build the machine-learning model may be divided in multipledata sets. In one or more embodiments, three data sets may be used indifferent stages of the creation of the machine-learning model:training, validation, and test sets.

Described machine-learning models may be initially fit on a trainingdata set, which is a set of examples used to fit parameters. Here,example training data sets suitable for preparing and/or trainingdescribed machine-learning processes may include data relating tohistoric business operations under historic circumstances, orcircumstances in certain enumerated scenarios, such as during a periodlow interest rates or relatively easy bank lending, or during a periodof highly restrictive fiscal policy implemented to control and addressundesirably high inflation. Such training sets may be correlated tosimilar training sets of user attributes 154 relating to particularattributes of the user. In the described example of client datum 108relating to a business, user attributes 154 may describe one or moreelements, datum, data and/or attributes relating to client engagementwith services provided by the user. For example, a business may requirefinancing to launch and can approach a bank (e.g., a type of user) forone or more types of loans. In this example, user attributes 154 maydescribe or relate to data describing retail, regional, or eveninvestment banks. In addition, user attributes may include datadescribing liquidity available to customers (e.g., clients) andperformance of outstanding loans and other products. In addition, clientdatum 108 may include data describing a pattern of activity or conductundertaken by the client regarding acquisition of goods or services fromthe user, depending on, for example, repayment behavior of the client tothe user for services rendered by the user to the client. In banking,that may mean that the client will assess risk in relatively difficultmacroeconomic conditions as dictated by higher-than-average federalinterest rates, etc.

In addition, in one or more embodiments, computing device 104 isconfigured to receive at least an element of user datum 112. For thepurpose of this disclosure, a “user datum” is an element, datum, orelements of data describing an amount of payment that the user wants toget from the client (e.g., for services the user rendered to the client,etc.). In addition, user datum 112 may describe user information, workhabits, skill, client relationships, and the like. Further, in someembodiments, the user datum includes a target datum that at leastgenerally describes resource transfer data from the client to the user.For example, such resource transfer data may include descriptions ofrepeat monetary payments from the client to the user over a specifiedduration relating to compensation for services rendered. In othercircumstances, such user datum 112 implementing additionalorganizational structure, offering different services or productsreflective of ongoing changes in client preferences, or other changes inexisting services or products, management of resources, and the like.More particularly, in some instances, the “user datum” may bealternatively referred to as a “service provider datum” and thereby alsobe based on data describing practical implementation of ideas thatresult in the introduction of new goods or services or improvement inoffering goods or services. Identification of user datum 112 may use amachine-learning model to analyze, for example, a pattern demonstratedby the user regarding achieving target datum 118, as also indicated byclient datum 108.

In addition, in one or more embodiments, computing device 104 isconfigured to receive at least an element of transfer datum 116. For thepurpose of this disclosure, a “transfer datum” is an element, datum, orelements of data describing resource, material, and/or monetary transferfrom the client to the user for services rendered by the user to theclient. For example, transfer datum 116 may describe one or moreperiodic monetary payments made by the client, such as a business or anaspiring athlete, to a user, such as a service provider including a bankor a personal trainer, etc. In addition, described processes mayaggregate multiple instances of transfer datum 116 to generate resourcetransfer data, which may chronologically track payment or repaymentbehavior from the client to the user.

More particularly, transfer datum 116 may be generated by computingdevice 104 (as to be further described below) as a function of clientdatum 108 and/or user datum 112. In the context of banking, transferdatum 116 may describe routine repayments, such as by a mortgagor (e.g.,borrower) to the mortgagee (e.g., lender). In the context of banking inchallenging macroeconomic circumstances as dictated byhigher-than-expected federal interest rates, transfer datum 116 may bereflect reductions in repayment from a maximum, or expected amount, or aminimum amount to prevent the account from going into collections.

More particularly, in some embodiments, generating transfer datum 116 asa function of user datum 112 may include digitally assessing one or morecategories of relating to repayment behavior the client demonstrated inresponse to various surrounding circumstances, such as macroeconomicconditions. In addition, one or more instances of transfer datum 116 maybe classified, by classifier 124 of machine-learning module 120 executedby processor 144, to client datum 108 and/or user datum 112.

Accordingly, concepts relating to transfer, such as periodic monetarytransfer, can be quantified by one or more elements, datum or data andthereby processed by “machine-learning processes” executed bymachine-learning module 120 of computing device 104 to, for example, beevaluated prior to display of multiple instances of transfer datum 116(e.g., a first transfer datum and at least a second transfer datum, eachrespectively describing, for a payment) hierarchically based on at leastuser-input datum 224A in user input field 148. More particularly, and asdescribed further herein with relation to FIG. 4 , a “machine-learningprocess,” as used in this disclosure, is a process that automatedly usestraining data to generate an algorithm that will be performed by acomputing device/module (e.g., computing device 104 of FIG. 1 ) toproduce outputs given data provided as inputs. Any machine-learningprocess described in this disclosure may be executed by machine-learningmodule 120 of computing device 104 to manipulate and/or process transferdatum 116 relating to describing instances or characteristics ofconfidence for the user.

“Training data,” as used herein, is data containing correlations that amachine-learning process may use to model relationships between two ormore categories of data elements. For instance, and without limitation,training data, in this instance, may include multiple data entries, eachentry representing a set of data elements that were recorded, received,and/or generated together and described various confidence levels ortraits relating to demonstrations of confidence. Data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple categories of data elements maybe related in training data according to various correlations, which mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. In addition, training data may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements.

For instance, a supervised learning algorithm (or any othermachine-learning algorithm described herein) may include one or moreinstances of transfer datum 116 describing confidence of a user asdescribed above as inputs. Accordingly, computing device 104 of FIG. 1may receive user-input datum 224A into input field 148 of display device132. User-input datum 224A may describe data for selecting a preferredattribute (e.g., pay off the full amount of an outstanding credit cardbalance, pay off a minimum required amount, pay an intermediary amount,etc.) of repayment behavior described by, for example transfer datum116. In addition, in some embodiments, either the client or the user maydictate target datum 118, which may be, for example, either a maximumrepayment amount permitted (e.g., typically the entirety of theoutstanding balance), a minimum required payment (e.g., a minimummonthly repayment as dictated by a credit card member agreement, etc.).Classifier 124 of machine-learning module 120 may classify one or moreinstances of transfer datum 116 relative to, for example, target datum118 (e.g., also in the context of confidence, such as achieving anoptimum confidence level). Accordingly, in some embodiments, classifier124 may classify instances of transfer datum 116 that more closelyrelate to or resemble target datum 118 within a closer proximity totarget datum 118.

In addition, in one or more embodiments, initiation of resource transferdescribed by the target datum may be triggered in response to a patternrepresentative of client interaction with the user exceeding thresholddatum 122. That is, in the context of personal training, threshold datum122 occur after two initial complimentary sessions. Accordingly, billingfrom the gym to the aspiring athlete will initiate upon start of thethird session between the aspiring athlete and the personal training.The third session, in this context, is the threshold triggeringinitiation of resource transfer from the client (e.g., the aspiringathlete) to the user (e.g., the gym).

In this way, a scoring function representing a desired form ofrelationship to be detected between inputs and outputs may be used bydescribed machine learning processes. Such as scoring function may, forinstance, seek to maximize the probability that a given input (e.g.,data describing perseverance relating to confidence) and/or combinationof elements and/or inputs (e.g., data describing confidence overall) isassociated with a given output (e.g., hierarchical display of multipleinstances of transfer datum 116 describing confidence) to minimize theprobability that a given input (e.g., data describing potentialover-confidence or recklessness) is not associated with a given output(e.g., additional stimuli encouraging confident or borderline recklessbehavior).

Still referring to FIG. 1 , in one or more embodiments, aspects of thepresent disclosure are directed to apparatus 100 for generating aninstruction set for a user. Described processes are executed bycomputing device 104 including processor 144, which is configured toexecute any one or more of the described steps. Memory component 140 isconnected to processor 144 and contains instructions configuringprocessor to receive client datum 108 from a client. For example, in oneor more embodiments, the client datum may include one or more elements,datum and/or data describing client information, payment, and/or thelike. Accordingly, the client datum may describe various resources(e.g., monetary, land, intellectual property, and other forms ofintangible assets and the like) of the client and a pattern that isrepresentative of client interaction with the user.

In addition, memory component 140 contains instructions configuring theat least processor 144 to receive a user datum from the user. Moreparticularly, the user datum may include one or more elements, datumand/or data describing goal data, where goal data relates to an amountof payment that the user wants to get from the client (e.g., forservices the user rendered to the client, etc.). In addition, user datum112 may describe user information, work habits, skill, clientrelationships, and the like. Further, in some embodiments, the userdatum includes target datum 116 that at least generally describesresource transfer data from the client to the user. For example, suchresource transfer data may include descriptions of repeat monetarypayments from the client to the user over a specified duration relatingto compensation for services rendered.

Specific payment scenarios may include monthly payments from a business(e.g., the client) to its law firm (e.g., the user) relating totransactional, government, or litigation related law practice workhandled by the law firm on behalf of the business. In addition, or thealternative, other examples may relate to personal performance trainingimprovements, where an aspiring athlete (e.g., the client) hires apersonal trainer (e.g., the user) to systematically focus on nutrition,hydration, sleep, progressive resistance, and cardiovascular training ona bi-weekly basis for six to eight months. The personal trainer mayreceive routine payments from the aspiring athlete, where such paymentsare described by resource transfer data. In addition, the target datummay describe an optimal or a maximum payment desired by the user fromthe client. That is, in the context of personal training, bi-weeklysessions can cost $130 per hour, for a total of $1,040 per month whenpurchased individually. However, the user (e.g., the gym providingpersonal training services) can elect to discount such services whenbought as a monthly recurring package, setting a package price of $850per month for a minimum of 6 months. Accordingly, in one or moreembodiments, this monthly recurring discounted price can be representedby the target datum.

In some embodiments, initiation of resource transfer described by targetdatum 118 is triggered by the pattern representative of clientinteraction with the user exceeding threshold datum 122. That is, in thecontext of personal training, the threshold may be after two initialcomplimentary sessions. Accordingly, billing from the gym to theaspiring athlete will initiate upon start of the third session betweenthe aspiring athlete and the personal training. The third session, inthis context, is the threshold triggering initiation of resourcetransfer from the client (e.g., the aspiring athlete) to the user (e.g.,the gym).

In addition, or the alternative, classifier 124 of machine-learningmodule 120 may determine a user score based on user datum 112 relatingto one or more categories. In some instances, the user score may includework habit score, skill score, client relationship score, and the like.More particularly, classifier 124 may classify resource transfer dataand/or user datum 112 to client datum 108 and generate the user scorebased on proximity or similarity of resource transfer data to clientdatum 108. That is, if the client is routinely paying their bills andmeeting or exceeding user expectations, the user scope may becommensurate with such favorable repayment behavior and be high, orvice-versa.

Further, in one or more embodiments, client datum 108 and user datum 112may be classified by machine-learning module 120 of computing device 104into one or more categories (also alternatively referred to herein as“goal groups.”) More particularly, client datum 108 and user datum 112may be classified to, for example, one or more instances of transferdatum 116, target datum 118 and/or threshold datum 122 using classifier.The one or more goal groups may include, for example and withoutlimitation, a client category, a user category, and the like. In someinstance, the client category may include multiple sub-categories, suchas a client information category, payment category, and the like. Inaddition, the user group may include a user information category, goalcategory, work habit category, skill category, client relationshipcategory, and the like.

In addition, or the alternative, memory component 140 containsinstructions configuring processor 144 to classify client datum 108 anduser datum 112 to multiple categories (e.g., as shown by transfer objectdatabase 300 of FIG. 3 ), calculate target datum 118 based onclassification of client datum 108 and user datum 112 to at least one ofmultiple categories, and identify one or more instances of transferdatum 116, including a first transfer datum and at least a secondtransfer datum from resource transfer data. That is, the first transferdatum can represent a first monthly payment (e.g., $850 for April formonthly personal training services), and the second transfer datum canrepresent a second monthly payment (e.g., $850 for May for monthlypersonal training services).

Refining at least the first transfer datum includes classifying at leastthe first transfer datum to target datum 118 and ranking (e.g.,hierarchically) the first transfer datum and the second transfer datumrelative to target datum 118, such as whether the client paid less thatthe requested $850/mo. or skipped one or more payments entirely.Accordingly, in one or more embodiments, described processes can alsodetermine the threshold as a function of the user score and rankedaggregated or total payments. In some embodiments, threshold datum 122may be a minimum payment (e.g., $50/mo.) the user (e.g., gym) mustreceive from the client (aspiring athlete) and the threshold may bedetermined by classifying payment history as demonstrated by classifyingresource transfer data to user datum. More particularly, in one or moreembodiments, the threshold may be one or more of: (1) a smallest numberof the ranked total payments; (2) an average of the total payments;and/or (3) determined as a function of the user score by using theclassifier to classify user datum 112 to one or more described dataelements, such as client datum 108. More particularly, in one or moreembodiments, threshold datum 122 may describe the user's skill, workhabit, client relationship, and the like.

In addition, the memory contains instructions configuring the at least aprocessor to generate an interface query data structure (e.g., displayedby GUI 136 of display device 132) including input field 148 based onranking the first transfer datum and the second transfer datum. Moreparticularly, the interface query data structure configures displaydevice 132 to display input field 148 to the user and receive at least auser-input datum (e.g., user-input datum 224A) into the input field. Insome embodiments, user-input datum 224A describes data for selecting apreferred attribute of transfer data (e.g., one or more instances oftransfer datum 116) associated with one or more instances of rankings ofthe first transfer datum and the second transfer datum. Still further,the interface query data structure configures display device 132 todisplay the instruction set including displaying the first transferdatum and at least the second transfer datum hierarchically based on theuser-input datum.

In addition, in one or more embodiments, generating the interface querydata structure further includes retrieving data describing attributes ofthe user from a database communicatively connected to the processor andgenerating the interface query data structure based on the datadescribing attributes of the user. Further, in addition, or thealternative, generating the target datum further includes retrievingdata describing current preferences of the client (e.g., regardingresource transfer data from the client to the user for servicesrendered, etc.) between a minimum value and a maximum value from adatabase communicatively connected to the processor, and generating theinterface query data structure based on the data describing currentpreferences of the client.

As described earlier, generating the instruction set further includesclassifying at least the first transfer datum and the second transferdatum to the target datum, ranking the first transfer datum and thesecond transfer datum to the target datum, and adjusting the thresholdfor triggering resource transfer from the client to the user based onthe user-input datum. In addition, or the alternative, in one or moreembodiments, generating the instruction set further includes determiningthe threshold by classifying the pattern that is representative ofclient interaction with the user to the user datum.

In some embodiments, generating the instruction set further includesadjusting the pattern that is representative of client interaction withthe user based on threshold datum 122. In addition, in some instances,generating the instruction set further includes classifying client datum108 to one or more categories based on the pattern that isrepresentative of client interaction with the user.

In one or more embodiments, apparatus 100 is further configured toevaluate user-input datum 224A including classifying, by classifier 124one or more new instances of user-input datum 224A with the firsttransfer datum and the second transfer datum generating a consecutivetransfer datum based on the classification, and displaying the firsttransfer datum, the second transfer datum, and at least the consecutivetransfer datum hierarchically based on the classification of theconsecutive transfer datum to one or more new instances of theuser-input datum.

In addition, or the alternative, classifying client datum 108 and userdatum 112 to multiple categories further includes aggregating the firsttransfer datum and at least the second transfer datum based on theclassification and further classifying aggregated transfer data to datadescribing the pattern that is representative of client interaction withthe user. Also, in some instances, the interface query data structurefurther configures display device 132 to provide an articulatedgraphical display including multiple regions organized in a treestructure format, wherein each region provides one or more instances ofpoint of interaction between the user and the remote display device.

Still further, described processes executed by machine-learning module120 of computing device 104 may generate an output (e.g., the describedinstruction set and/or transfer data hierarchy 224B) inclusive of a textand/or digital media-based content describing a strategy recommendationas a function of, for example, target datum 118, client datum 108, andthe user score, where the strategy recommendation may also be generatedusing a machine learning model as to be further described below. In someinstances, the strategy recommendation may be configured to increase thethreshold or increase the payment from the client. In addition, or thealternative, the strategy recommendation may include a skillrecommendation to improve a skill of a user (e.g., such as providingPilates as a part of personal-training services), such as anorganizational skill recommendation, technical skill recommendation, andthe like. Still further, in some instances, the strategy recommendationmay include a client relationship recommendation, such as to improve arelationship between the client and the user. For example, the clientrelationship recommendation may include communication recommendation,networking recommendation, and the like.

In one or more particular embodiments, the strategy recommendation mayinclude a work habit recommendation to improve work habit of the user,such as a working time recommendation, efficiency recommendation, andthe like. In addition, or the alternative, the strategy recommendationmay include a support recommendation to improve a support structure ofthe user, such as a team recommendation, system recommendation, and thelike.

Still further, in one or more embodiments, client datum 108 may beclassified by classifier 124 into one or more client character groupsusing a client characteristic classification model. For example, as anon-limiting example, the client character groups may be described bydata relating to, for example, various traits such as being shy,extroverted, openness, conscientiousness, agreeableness, neuroticism,resilience, optimism, assertiveness, ambition, introverted, and thelike. In addition, or the alternative, the described instruction set maybe generated to include data describing, for example, a persuasionrecommendation as a function of the one or more client character groups.More particularly, the persuasion recommendation may a recommendationfor the user to improve persuasion skills to communicate the clients.

In some instances, in one or more embodiments, computing device 104 isconfigured to receive at least an element of target datum 118. Inaddition, or the alternative, computing device 104 is configured toreceive one or more instances of an “outlier cluster,” as used formethods described in U.S. patent application Ser. No. 18/141,320, filedon Apr. 28, 2023, titled “METHOD AND AN APPARATUS FOR ROUTINEIMPROVEMENT FOR AN ENTITY,” and, U.S. patent application Ser. No.18/141,296, filed on Apr. 28, 2023, titled “SYSTEMS AND METHODS FOR DATASTRUCTURE GENERATION,” both of which are incorporated herein byreference herein in their respective entireties. As described earlierand throughout this disclosure, a “target datum” is an element, datum,or elements of data describing a payment or repayment goal or objective,either short or long term, desired for achievement by the user.Accordingly, in this example, target datum 118 may be determined oridentified using one or more outlier clusters. More particularly,described machine-learning processes may use, as inputs, one or moreinstances of client datum 108, user datum 112, transfer datum 116,target datum 118 and/or threshold datum 122 in combination with theother data described herein, and use one or more associated outliercluster elements with target outputs, such as transfer data hierarchy224B. As a result, in some instances, classifier 124 may classify inputsto target outputs including associated outlier cluster elements togenerate transfer data hierarchy 224B.

In addition, and without limitation, in some cases, database 150 may belocal to computing device 104. In another example, and withoutlimitation, database 150 may be remote to computing device 104 andcommunicative with computing device 104 by way of one or more networks.A network may include, but is not limited to, a cloud network, a meshnetwork, and the like. By way of example, a “cloud-based” system canrefer to a system which includes software and/or data which is stored,managed, and/or processed on a network of remote servers hosted in the“cloud,” e.g., via the Internet, rather than on local severs or personalcomputers. A “mesh network” as used in this disclosure is a localnetwork topology in which computing device 104 connects directly,dynamically, and non-hierarchically to as many other computing devicesas possible. A “network topology” as used in this disclosure is anarrangement of elements of a communication network. Network may use animmutable sequential listing to securely store database 150. An“immutable sequential listing,” as used in this disclosure, is a datastructure that places data entries in a fixed sequential arrangement,such as a temporal sequence of entries and/or blocks thereof, where thesequential arrangement, once established, cannot be altered, orreordered. An immutable sequential listing may be, include and/orimplement an immutable ledger, where data entries that have been postedto the immutable sequential listing cannot be altered.

Database 150 may include keywords. As used in this disclosure, a“keyword” is an element of word or syntax used to identify and/or matchelements to each other. For example, without limitation, a keyword maybe “finance” in the instance that a business is seeking to optimizeoperations in the financial services and/or retirement industry. Inanother non-limiting example, keywords of a key-phrase may be “luxuryvehicle manufacturing” in an example where the business is seeking tooptimize market share internationally, or certain rapidly developingmarkets. Database 150 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art, upon reviewing the entirety of thisdisclosure, would recognize as suitable upon review of the entirety ofthis disclosure.

With continued reference to FIG. 1 , a “classifier,” as used in thisdisclosure is type or operational sub-unit of any describedmachine-learning model or process executed by machine-learning module120, such as a mathematical model, neural net, or program generated by amachine-learning algorithm known as a “classification algorithm” thatdistributes inputs into categories or bins of data, outputting thecategories or bins of data and/or labels associated therewith. Aclassifier may be configured to classify and/or output at least a datum(e.g., one or more instances of any one or more of client datum 108,user datum 112, transfer datum 116, and/or target datum 118 as well asother elements of data produced, stored, categorized, aggregated orotherwise manipulated by the described processes) that labels orotherwise identifies a set of data that are clustered together, found tobe close under a distance metric, or the like.

Referring again to FIG. 1 , computing device 104 may be configured toidentifying business impact by using classifier 124 to classify one ormore instances of any one or more of client datum 108, user datum 112,transfer datum 116, and/or target datum 118 based on user attributes 154and/or category data 158. Accordingly, classifier 124 ofmachine-learning module 120 may classify attributes within userattributes 154 related to demonstrating one or more repayment behaviorstoward reaching or exceeding target datum 118.

In addition, in some embodiments, machine-learning module 120 performingthe described correlations may be supervised. Alternatively, in otherembodiments, machine-learning module 120 performing the describedcorrelations may be unsupervised. In addition, classifier 124 may labelvarious data (e.g., one or more instances of any one or more of clientdatum 108, user datum 112, transfer datum 116, and/or target datum 118as well as other elements of data produced, stored, categorized,aggregated, or otherwise manipulated by the described processes) usingmachine-learning module 120. For example, machine-learning module 120may label certain relevant parameters of one or more instances of clientdatum 108 with parameters of one or more user attributes 154.

In addition, machine-learning processes performed by machine-learningmodule 120 may be trained using one or more instances of category data158 to, for example, more heavily weigh or consider instances ofcategory data 158 deemed to be more relevant to the business. Morespecifically, in one or more embodiments, category data 158 may be basedon or include correlations of parameters associated with client datum108 to parameters of user attributes 154. In addition, category data 158may be at least partially based on earlier iterations ofmachine-learning processes executed by machine-learning module 120. Insome instances, running machine-learning module 120 over multipleiterations refines correlation of parameters or data describing entityoperations (e.g., associated with client datum 108) with parametersdescribing at least user attributes 154.

Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naïveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers.

Still referring to FIG. 1 , computing device 104 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)+P(B), where P(AB) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 104 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 104 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , computing device 104 may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

Further referring to FIG. 1 , generating k-nearest neighbors algorithmmay generate a first vector output containing a data entry cluster,generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute/as derived using aPythagorean norm

${l = \sqrt{\sum_{i = 0}^{n}a_{i}^{2}}},$where α_(i) is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes; this may, for instance, be advantageous wherecases represented in training data are represented by differentquantities of samples, which may result in proportionally equivalentvectors with divergent values.

Referring now to FIGS. 2A-2B, exemplary embodiments of user input field148 as configured to be displayed by GUI 136 of display device 132 basedon an interface query data structure are illustrated. As definedearlier, an “interface query data structure” refers to, for example, adata organization format used to digitally request a data result oraction on the data (e.g., stored in a database). In one or moreembodiments, each output screen 200A-200B may be an example of an outputscreen configured to be displayed by display device 132 of FIG. 1 by thedescribed interface query data structure. That is, more particularly,the described interface query data structure may configure displaydevice 132 of FIG. 1 to display any one or more of output screens200A-200B as described in the present disclosure. Accordingly, outputscreen 200A may include multiple forms of indicia.

In one or more embodiments, output screen 200A and output screen 200Bmay be examples of user input field 148 and/or GUI 136 as displayed bydisplay device 132, which may be a “smart” phone, such as an iPhone, orother electronic peripheral or interactive cell phone, tablet, etc.Output screen 200A may be a screen initially displayed to a user (e.g.,a human or a human representing or acting on behalf of a business orsome other entity, and have user engagement area 208 includingidentification field 204A, client resource data field 212A, resourcetransfer field 216A, user-input field 220A, which may include one ormore instances of user-input datum 224A describing data for selecting apreferred attribute of any one or more repayment behaviors associatedwith one or more instances of client datum 108.

In addition, in one or more embodiments, user-input datum 224A may bereflective of and/or provide a basis for user attributes 154. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which fewer or additional interactive userinput fields may be displayed by screen 208A. Identification field 204Amay identify described processes performed by processor 144 of computingdevice 104 by displaying identifying indicia, such as “Client-UserInteraction Summary” as shown in FIG. 2A to permit, for example, a humanto interact with GUI 136 and input information relating to a field ofchoice (e.g., business operations), through (for example) interactivityprovided by identification field 204A.

Such information can include data describing activities performed by thebusiness relating to the business achieving its defined goal (e.g.,target datum 118 of FIG. 1 ). In some instances, a human may select fromone or more options (not shown in FIG. 2A) relating to prompts providedby identification field 204A to input such information relating tospecific details of, for example, the business. In addition, in someembodiments, any of the described fields may include interactivefeatures, such as prompts, permitting for a human to select additionaltextual and/or other digital media options further identifying orclarifying the nature of the business relating to the respectivespecifics of that field. For example, client resource data field 212Amay display assessments of corresponding instruction sets regardingrelevance and potential for positive impact on the business and maythereby also provide interactive features permitting the human to inputadditional data or information relating to expectations of positive ofnegative assessments for a given instruction set. Such additionalhuman-input data may be computationally evaluated by describedmachine-learning processes executed by machine-learning module 120 andthereby correspondingly appear in the described progression sequence.

Like output screen 200A, output screen 200B may be an example of ascreen subsequently shown to a human as described earlier based onhuman-provided input to any one or more of the displayed fields. Thatis, output screen 200B may display “Instruction Set for User” inidentification field 204B as indicating completion of intake ofhuman-provided input and that described machine-learning processes havecompleted described classifying processes to output customized skillfactor assessment area 208B to the user. For example, in one or moreembodiments, customized repayment assessment area 208B may also includemultiple human-interactive fields, including threshold identificationfield 212B, client interaction field 216B, preferred attribute field220B, and transfer data hierarchy 224B generated as described earlier.

Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which fewer or additionalinteractive human input fields may be displayed by output screen 200B.Each field within customized repayment assessment area 208B may displayany combination of human interactive text and/or digital media, eachfield intending to provide specific data-driven feedback directed tooptimizing ongoing business performance of the business. Various exampletypes of specifics (e.g., “decrease risky leverage in high interest rateconditions”) are shown in customized skill factor assessment area 208B,but persons skilled in the art will be aware of other example types offeedback, each of which being generated as suitable for a given businessby processor 144. In addition, in one or more embodiments, any one ormore fields of customized skill factor assessment area 208B may behuman-interactive, such as by posing a query for the human to providefeedback in the form of input such that described machine-learningprocesses performed by machine-learning module 120 may intake refinedinput data and correspondingly process related data and provide anupdated customized repayment assessment area 208B. In some embodiments,such processes may be performed iteratively, thereby allowing forongoing refinement, redirection, and optimization of customizedrepayment assessment area 208B to better meet the needs of the client oruser.

Referring now to FIG. 3 , an exemplary embodiment of transfer objectivedatabase 300 is illustrated. In one or more embodiments, transferobjective database 300 may be an example of database 150 of FIG. 1 .Query database may, as a non-limiting example, organize data stored inthe user activity database according to one or more database tables. Oneor more database tables may be linked to one another by, for instance,common column values. For instance, a common column between two tablesof expert database may include an identifier of a query submission, suchas a form entry, textual submission, or the like, for instance asdefined below; as a result, a query may be able to retrieve all rowsfrom any table pertaining to a given submission or set thereof. Othercolumns may include any other category usable for organization orsubdivision of expert data, including types of query data, identifiersof interface query data structures relating to obtaining informationfrom the user, times of submission, or the like. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious ways in which user activity data from one or more tables may belinked and/or related to user activity data in one or more other tables.

In addition, in one or more embodiments, computing device 104 may beconfigured to access and retrieve one or more specific types of userattributes 154 and/or other data types, e.g., one or more instance ofclient datum 108, user datum 112, transfer datum 116 and/or target datum118, as well as threshold datum 122, categorized in multiple tables fromtransfer objective database 300. For example, as shown in FIG. 3 ,transfer objective database 300 may be generated with multiplecategories including client group datum 304, user group datum 308,target transfer group 312 and work habit 316. Consequently, describedprocesses may classify one or more instances of client datum 108 fromclient group datum 304 to user datum 112 and/or user-input datum 224Athat may be input user input field 148 of FIG. 1 . In some instances,user-input datum 224A may describe data for selecting a preferredattribute of any one or more skills associated with one or moreinstances of transfer datum 116. In addition, described processes mayretrieve data describing additional attributes related to the preferredattribute of transfer datum 116 from transfer objective database 300connected with the processor based on client group datum 304 (e.g., or,alternatively, one or more of user group datum 308, target transfergroup 312, and/or work habit 316, etc.).

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. In one or more embodiments,machine-learning module 400 may be an example of machine-learning module120 of computing device 104 of FIG. 1 . Machine-learning module 120 mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure usingmachine-learning processes. A “machine-learning process,” as used inthis disclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a computingdevice/module (e.g., computing device 104 of FIG. 1 ) to produce outputs408 given data provided as inputs 412; this is in contrast to anon-machine-learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include multipledata entries, each entry representing a set of data elements that wererecorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training data404 may evince one or more trends in correlations between categories ofdata elements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data 404 according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data 404 may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data 404 may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data 404 may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data 404 may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively, or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

As a non-limiting illustrative example, input data may include one ormore instances of any one or more of client datum 108, user datum 112,transfer datum 116, and/or target datum 118, as well as category data158 and/or user attributes 154, to provide the instruction set as may bedetermined as described earlier, such as where at least some instancesof the transfer datum 116 exceeding a threshold (e.g., that may beuser-defined and input into user input field 148, or externally defined)are aggregated to define and display the instruction set to the user. Inaddition, in one or more embodiments, the interface query data structureas described herein includes one or more interface query datastructures, any one of which may include an interface that defines a setof operations supported by a data structure and related semantics, ormeaning, of those operations. For example, in the context of personalperformance improvement coaching, interface query data structure mayinclude one or more interface query data structures that may appear tothe user in the form of one or more text-based or other digitalmedia-based surveys, questionnaires, lists of questions, examinations,descriptions, etc.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine-learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. A distance metric may include any norm,such as, without limitation, a Pythagorean norm. Machine-learning module400 may generate a classifier using a classification algorithm, definedas a process whereby a computing device and/or any module and/orcomponent operating thereon derives a classifier from training data 404.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naïveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers. As anon-limiting example, training data classifier 416 may classify elementsof training data to iteratively refine the instruction set to reflectthe user's preferences, such as by preparing transfer data hierarchy224B for the user to more effectively and/or efficiently progress tomatch target datum 118.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine-learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively, or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesfor providing a skill factor (e.g., of transfer datum 116) hierarchy toa user. As a further non-limiting example, a machine-learning model 424may be generated by creating an artificial neural network, such as aconvolutional neural network comprising an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training data 404 set are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude one or more instances of any one or more of client datum 108,user datum 112, transfer datum 116, and/or target datum 118, as well ascategory data 158 and/or user attributes 154 as described above asinputs, transfer data hierarchy 224B and/or similar textual and/orvisual imagery (e.g., digital photos and/or videos) relating toproviding transfer data hierarchy 224B to a user as outputs, and ascoring function representing a desired form of relationship to bedetected between inputs and outputs; scoring function may, for instance,seek to maximize the probability that a given input and/or combinationof elements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 404. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 428 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 4 , machine-learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g., a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g., a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminant analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized trees, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring to FIG. 5 , an exemplary embodiment of fuzzy set comparison500 is illustrated. In one or more embodiments, data describing anydescribed process relating to providing a skill factor hierarchy to auser as performed by processor 144 of computing device 104 may includedata manipulation or processing including fuzzy set comparison 500. Inaddition, in one or more embodiments, usage of an inference enginerelating to data manipulation may involve one or more aspects of fuzzyset comparison 500 as described herein. That is, although discreteinteger values may be used as data to describe, for example, one or moreinstances of any one or more of client datum 108, user datum 112,transfer datum 116, and/or target datum 118, as well as category data158 and/or user attributes 154, fuzzy set comparison 500 may bealternatively used. For example, a first fuzzy set 504 may berepresented, without limitation, according to a first membershipfunction 508 representing a probability that an input falling on a firstrange of values 512 is a member of the first fuzzy set 504, where thefirst membership function 508 has values on a range of probabilitiessuch as without limitation the interval [0,1], and an area beneath thefirst membership function 508 may represent a set of values within firstfuzzy set 504. Although first range of values 512 is illustrated forclarity in this exemplary depiction as a range on a single number lineor axis, first range of values 512 may be defined on two or moredimensions, representing, for instance, a Cartesian product between aplurality of ranges, curves, axes, spaces, dimensions, or the like.First membership function 508 may include any suitable function mappingfirst range of values 512 to a probability interval, including withoutlimitation a triangular function defined by two linear elements such asline segments or planes that intersect at or below the top of theprobability interval. As a non-limiting example, triangular membershipfunction may be defined as:

${y( {x,a,b,c} )} = \{ \begin{matrix}{0,{{{for}x} > {c{and}x} < a}} \\{\frac{x - a}{b - a},{{{for}a} \leq x < b}} \\{\frac{c - x}{c - b},{{{if}b} < x \leq c}}\end{matrix} $a trapezoidal membership function may be defined as:

${y( {x,a,b,c,d} )} = {\max( {{\min\ ( {\frac{x - a}{b - a},1,\frac{d - x}{d - c}} )},0} )}$a sigmoidal function may be defined as:

${y( {x,a,c} )} = \frac{1}{1 - e^{- {a({x - c})}}}$a Gaussian membership function may be defined as:

${y( {x,c,\sigma} )} = e^{{- \frac{1}{2}}{(\frac{x - c}{\sigma})}^{2}}$and a bell membership function may be defined as:

${y( {x,a,b,c,} )} = \lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \rbrack^{- 1}$Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 5 , first fuzzy set 504 may represent any valueor combination of values as described above, including output from oneor more machine-learning models, one or more instances of any one ormore of client datum 108, user datum 112, transfer datum 116, and/ortarget datum 118, as well as category data 158 and/or user attributes154, and a predetermined class, such as without limitation, query dataor information including interface query data structures stored intransfer objective database 300 of FIG. 3 . A second fuzzy set 516,which may represent any value which may be represented by first fuzzyset 504, may be defined by a second membership function 520 on a secondrange of values 524; second range of values 524 may be identical and/oroverlap with first range of values 512 and/or may be combined with firstrange via Cartesian product or the like to generate a mapping permittingevaluation overlap of first fuzzy set 504 and second fuzzy set 516.Where first fuzzy set 504 and second fuzzy set 516 have a region 528that overlaps, first membership function 508 and second membershipfunction 520 may intersect at a point 532 representing a probability, asdefined on probability interval, of a match between first fuzzy set 504and second fuzzy set 516. Alternatively, or additionally, a single valueof first and/or second fuzzy set may be located at a locus 536 on firstrange of values 512 and/or second range of values 524, where aprobability of membership may be taken by evaluation of first membershipfunction 508 and/or second membership function 520 at that range point.A probability at 528 and/or 532 may be compared to a threshold 540 todetermine whether a positive match is indicated. Threshold 540 may, in anon-limiting example, represent a degree of match between first fuzzyset 504 and second fuzzy set 516, and/or single values therein with eachother or with either set, which is sufficient for purposes of thematching process; for instance, threshold may indicate a sufficientdegree of overlap between an output from one or more machine-learningmodels one or more instances of any one or more of client datum 108,user datum 112, transfer datum 116, and/or target datum 118, as well ascategory data 158 and/or user attributes 154 and a predetermined class,such as without limitation, query data categorization, for combinationto occur as described above. Alternatively, or additionally, eachthreshold may be tuned by a machine-learning and/or statistical process,for instance and without limitation as described in further detailbelow.

Further referring to FIG. 5 , in an embodiment, a degree of matchbetween fuzzy sets may be used to classify one or more instances of anyone or more of client datum 108, user datum 112, transfer datum 116,and/or target datum 118, to as well as category data 158 and/or userattributes 154 stored in transfer objective database 300. For instance,if client datum 108 and/or interface query data structure 112 has afuzzy set matching certain interface query data structure data valuesstored in transfer objective database 300 (e.g., by having a degree ofoverlap exceeding a threshold), computing device 104 may classify one ormore instances of any one or more of client datum 108, user datum 112,transfer datum 116, and/or target datum 118 as belonging to userattributes 154 (e.g., aspects of user behavior as demonstrated by userattributes 154 of FIG. 1 and/or user group datum 308 of FIG. 3 relatingto user commitment towards achieving target datum 118). Where multiplefuzzy matches are performed, degrees of match for each respective fuzzyset may be computed and aggregated through, for instance, addition,averaging, or the like, to determine an overall degree of match.

Still referring to FIG. 5 , in an embodiment, client datum 108 and/oruser datum 112 may be compared to multiple transfer objective database300 categorization fuzzy sets. For instance, client datum 108 and/oruser datum 112 may be represented by a fuzzy set that is compared toeach of the multiple transfer objective database 300 categorizationfuzzy sets; and a degree of overlap exceeding a threshold between theclient datum 108 and/or user datum 112 fuzzy set and any of the transferobjective database 300 categorization fuzzy sets may cause computingdevice 104 to classify one or more instances of any one or more ofclient datum 108, user datum 112, transfer datum 116, and/or targetdatum 118 as belonging to one or more corresponding interface query datastructures associated with transfer objective database 300categorization (e.g., selection from categories in transfer objectivedatabase 300, etc.). For instance, in one embodiment there may be twotransfer objective database 300 categorization fuzzy sets, representing,respectively, transfer objective database 300 categorization (e.g., intoeach of client group datum 304, user group datum 308, target transfergroup 312, and/or work habit 316). For example, a First transferobjective database 300 categorization may have a first fuzzy set; aSecond transfer objective database 300 categorization may have a secondfuzzy set; and one or more instances of any one or more of client datum108, user datum 112, transfer datum 116, and/or target datum 118, to aswell as category data 158 and/or user attributes 154 may each have acorresponding fuzzy set.

Computing device 104, for example, may compare one or more instances ofany one or more of client datum 108, user datum 112, transfer datum 116,and/or target datum 118, to as well as category data 158 and/or userattributes 154 fuzzy sets with fuzzy set data describing each of thecategories included in transfer objective database 300, as describedabove, and classify one or more instances of any one or more of clientdatum 108, user datum 112, transfer datum 116, and/or target datum 118,to as well as category data 158 and/or user attributes 154 to one ormore categories (e.g., client group datum 304, user group datum 308,target transfer group 312, and/or work habit 316). Machine-learningmethods as described throughout may, in a non-limiting example, generatecoefficients used in fuzzy set equations as described above, such aswithout limitation x, c, and a of a Gaussian set as described above, asoutputs of machine-learning methods. Likewise, any described datumherein may be used indirectly to determine a fuzzy set, as, for example,client datum 108 fuzzy set and/or user datum 112 fuzzy set may bederived from outputs of one or more machine-learning models that takeclient datum 108 and/or user datum 112 directly or indirectly as inputs.

Still referring to FIG. 5 , a computing device may use a logiccomparison program, such as, but not limited to, a fuzzy logic model todetermine a transfer objective database 300 response. A transferobjective database 300 response may include, but is not limited to,accessing and/or otherwise communicating with any one or more of clientgroup datum 304, user group datum 308, target transfer group 312, workhabit 316, and the like; each such transfer objective database 300response may be represented as a value for a linguistic variablerepresenting transfer objective database 300 response or in other wordsa fuzzy set as described above that corresponds to a degree of matchingbetween data describing client datum 108 and/or user datum 112 and oneor more categories within transfer objective database 300 as calculatedusing any statistical, machine-learning, or other method that may occurto a person skilled in the art upon reviewing the entirety of thisdisclosure.

In some embodiments, determining a transfer objective database 300categorization may include using a linear regression model. A linearregression model may include a machine-learning model. A linearregression model may be configured to map data of client datum 108and/or user datum 112, to one or more transfer objective database 300parameters. A linear regression model may be trained using amachine-learning process. A linear regression model may map statisticssuch as, but not limited to, quality of client datum 108 and/or userdatum 112. In some embodiments, determining transfer objective database300 of client datum 108 and/or user datum 112 may include using atransfer objective database 300 classification model. A transferobjective database 300 classification model may be configured to inputcollected data and cluster data to a centroid based on, but not limitedto, frequency of appearance, linguistic indicators of quality, and thelike. Centroids may include scores assigned to them such that quality ofclient datum 108 and/or user datum 112 may each be assigned a score.

In some embodiments, transfer objective database 300 classificationmodel may include a K-means clustering model. In some embodiments,transfer objective database 300 classification model may include aparticle swarm optimization model. In some embodiments, determining thetransfer objective database 300 of client datum 108 and/or user datum112 may include using a fuzzy inference engine (e.g., to assess theprogress of the user and use said data to amend or generate newstrategies based on user progress). A fuzzy inference engine may beconfigured to map one or more instances of any one or more of clientdatum 108, user datum 112, transfer datum 116, and/or target datum 118,to as well as category data 158 and/or user attributes 154 data elementsusing fuzzy logic. In some embodiments, described datum may be arrangedby a logic comparison program into transfer objective database 300arrangement. A “transfer objective database 300 arrangement” as used inthis disclosure is any grouping of objects and/or data based onsimilarity to each other and/or relation to providing transfer datahierarchy 224B of FIG. 2B to the user for the user to achieve. This stepmay be implemented as described above in FIG. 1 .

Membership function coefficients and/or constants as described above maybe tuned according to classification and/or clustering algorithms. Forinstance, and without limitation, a clustering algorithm may determine aGaussian or other distribution of questions about a centroidcorresponding to a given scoring level, and an iterative or other methodmay be used to find a membership function, for any membership functiontype as described above, that minimizes an average error from thestatistically determined distribution, such that, for instance, atriangular or Gaussian membership function about a centroid representinga center of the distribution that most closely matches the distribution.Error functions to be minimized, and/or methods of minimization, may beperformed without limitation according to any error function and/orerror function minimization process and/or method as described in thisdisclosure.

Further referring to FIG. 5 , an inference engine may be implemented toassess the progress of the user and use said data to amend or generatenew strategies based on user progress according to input and/or outputmembership functions and/or linguistic variables. For instance, a firstlinguistic variable may represent a first measurable value pertaining toclient datum 108 and/or user datum 112, such as a degree of matchingbetween data describing user aspirations and strategies based onresponses to interface query data structures stored in transferobjective database 300. Continuing the example, an output linguisticvariable may represent, without limitation, a score value. An inferenceengine may combine rules, such as: “if the demonstrated commitment levelof a person or business falls beneath a threshold,” and “the observedperformance of the person or business relative to their or its peers isdeficient,” the commitment score is ‘deficient“— the degree to which agiven input function membership matches a given rule may be determinedby a triangular norm or “T-norm” of the rule or output membershipfunction with the input membership function, such as min (a, b), productof a and b, drastic product of a and b, Hamacher product of a and b, orthe like, satisfying the rules of commutativity (T(a, b)=T(b, a)),monotonicity: (T(a, b)≤T(c, d) if a≤c and b≤d), (associativity: T(a,T(b, c))=T(T(a, b), c)), and the requirement that the number 1 acts asan identity element. Combinations of rules (“and” or “or” combination ofrule membership determinations) may be performed using any T-conorm, asrepresented by an inverted T symbol or “⊥” such as max(a, b),probabilistic sum of a and b (a+b−a*b), bounded sum, and/or drasticT-conorm; any T-conorm may be used that satisfies the properties ofcommutativity: ⊥(a, b)=⊥(b, a), monotonicity: ⊥(a, b)≤⊥(c, d) if a≤c andb≤d, associativity: ⊥(a, ⊥(b, c))=⊥(⊥(a, b), c), and identity element of0. Alternatively, or additionally T-conorm may be approximated by sum,as in a “product-sum” inference engine in which T-norm is product andT-conorm is sum. A final output score or other fuzzy inference outputmay be determined from an output membership function as described aboveusing any suitable defuzzification process, including without limitationMean of Max defuzzification, Centroid of Area/Center of Gravitydefuzzification, Center Average defuzzification, Bisector of Areadefuzzification, or the like. Alternatively, or additionally, outputrules may be replaced with functions according to the Takagi-Sugeno-King(TSK) fuzzy model.

Now referring to FIG. 6 , method 600 for generating an instruction setfor a user is described. At step 605, method 600 includes receiving, bya computing device, a client datum from a client, wherein the clientdatum describes resources of the client and a pattern that isrepresentative of client interaction with the user. This step may beimplemented as described above, without limitation, in FIGS. 1-7 .

Still referring to FIG. 6 , at step 610, method 600 includes receiving,by the computing device, a user datum from the user, the user datumincluding a target datum that describes resource transfer data from theclient to the user, wherein initiation of resource transfer described bythe target datum is triggered by the pattern exceeding a threshold. Thisstep may be implemented as described above, without limitation, in FIGS.1-7 .

Still referring to FIG. 6 , at step 615, method 600 includesclassifying, by the computing device, the client datum, and the userdatum to a plurality of categories. This step may be implemented asdescribed above, without limitation, in FIGS. 1-7 .

Still referring to FIG. 6 , at step 620, method 600 includescalculating, by the computing device, the target datum based onclassification of the client datum and the user datum to the pluralityof categories. The interface query data structure configures displaydevice 132 to display the input field to the user. This step may beimplemented as described above, without limitation, in FIGS. 1-7 .

Still referring to FIG. 6 , at step 625, method 600 includesidentifying, by the computing device, a first transfer datum and atleast a second transfer datum from transfer data, wherein refining atleast the first transfer datum comprises classifying, by the computingdevice, at least the first transfer datum to the target datum; andranking, by the computing device, the first transfer datum and thesecond transfer datum relative to the target datum.

Still referring to FIG. 6 , at step 630, method 600 includes generating,by the computing device, an interface query data structure including aninput field based on ranking the first transfer datum and the secondtransfer datum, wherein the interface query data structure configures aremote display device to display the input field to the user; receive atleast a user-input datum into the input field, wherein the user-inputdatum describes data for selecting a preferred attribute of transferdata associated with one or more instances of rankings of the firsttransfer datum and the second transfer datum; and display theinstruction set including displaying the first transfer datum and atleast the second transfer datum hierarchically based on the user-inputdatum.

In addition, the interface query data structure configures displaydevice 132 to receive at least user-input datum 224A into user inputfield 148. User-input datum 224A describes data for selecting apreferred attribute of any one or more skills associated with one ormore instances of the aggregated first skill factor datum and at leastthe second skill factor datum. In addition, the interface query datastructure configures display device 132 to display the first skillfactor and at least the second skill factor datum hierarchically basedon user-input datum 224A.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay device 736, discussed further below. Input device 732 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Video display adapter 752 and display device 736 may beutilized in combination with processor 704 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 700 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 712 via a peripheral interface 756.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate toprovide a multiplicity of feature combinations in associated newembodiments. Furthermore, while the foregoing describes several separateembodiments, what has been described herein is merely illustrative ofthe application of the principles of the present invention.Additionally, although particular methods herein may be illustratedand/or described as being performed in a specific order, the ordering ishighly variable within ordinary skill to achieve methods, apparatus, andsoftware according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for generating an instruction setfor a user, the apparatus comprising: at least a processor; a memoryconnected to the processor, the memory containing instructionsconfiguring the at least a processor to: receive a client datum from aclient device, wherein the client datum describes resources of theclient device and a pattern that is representative of clientinteractions; receive a user datum from the user; classify the clientdatum and the user datum to a category of a plurality of categories;calculate a target datum based on the category; identify a firsttransfer datum and at least a second transfer datum from transfer data,wherein identifying at least the first transfer datum comprises:classifying the target datum to the at least the first transfer datum;and generate an interface query data structure including an input fieldbased on ranking the first transfer datum and the second transfer datum,wherein the interface query data structure configures a remote displaydevice to: display the input field to the user; receive at least auser-input datum into the input field, wherein the user-input datumdescribes data for selecting a preferred attribute of resource transferdata associated the first transfer datum; and display the instructionset including displaying the first transfer datum based on theuser-input datum.
 2. The apparatus of claim 1, wherein generating theinterface query data structure further comprises: retrieving datadescribing attributes of the user from a database communicativelyconnected to the processor; and generating the interface query datastructure based on the data describing attributes of the user.
 3. Theapparatus of claim 1, wherein generating the target datum furthercomprises: retrieving data describing current preferences of the clientdevice between a minimum value and a maximum value from a databasecommunicatively connected to the processor; and generating the interfacequery data structure based on the data describing current preferences ofthe client device.
 4. The apparatus of claim 1, wherein generating theinstruction set further comprises: classifying at least the firsttransfer datum and the second transfer datum to the target datum;ranking the first transfer datum and the second transfer datum to thetarget datum; and adjusting a threshold datum for triggering resourcetransfer from the client device to the user based on the user-inputdatum.
 5. The apparatus of claim 1, wherein generating the instructionset further comprises: determining a threshold datum by classifying thepattern that is representative of client interaction with the user tothe user datum.
 6. The apparatus of claim 5, wherein generating theinstruction set further comprises: adjusting the pattern that isrepresentative of client interaction with the user based on thethreshold datum.
 7. The apparatus of claim 1, wherein generating theinstruction set further comprises: classifying the client datum to oneor more of the plurality of categories based on the pattern that isrepresentative of client interaction with the user.
 8. The apparatus ofclaim 1, further configured to evaluate the user-input datum comprising:classifying one or more new instances of the user-input datum with atleast the first transfer datum; generating a consecutive transfer datumbased on the classification; and displaying the first transfer datum andat least the consecutive transfer datum hierarchically based onclassification of the consecutive transfer datum to one or more newinstances of the user-input datum.
 9. The apparatus of claim 1, whereinclassifying the client datum and the user datum to a plurality ofcategories further comprises: aggregating at least the first transferdatum based on the classification; and further classifying aggregatedtransfer data to data describing the pattern that is representative ofclient interaction with the user.
 10. The apparatus of claim 1, whereinthe interface query data structure further configures the remote displaydevice to provide an articulated graphical display including multipleregions organized in a tree structure format, wherein each regionprovides one or more instances of point of interaction between the userand the remote display device.
 11. A method for generating aninstruction set for a user, the method comprising: receiving, by acomputing device, a client datum from a client device, wherein theclient datum describes resources of the client device and a pattern thatis representative of client interactions; receiving, by the computingdevice, a user datum from the user; classifying, by the computingdevice, the client datum and the user datum to a category of a pluralityof categories; calculating, by the computing device, a target datumbased on the category; identifying, by the computing device, a firsttransfer datum and at least a second transfer datum from transfer data,wherein identifying at least the first transfer datum comprises:classifying, by the computing device, the target datum to the at leastthe first transfer datum; and generating, by the computing device, aninterface query data structure including an input field based on rankingthe first transfer datum and the second transfer datum, wherein theinterface query data structure configures a remote display device to:display the input field to the user; receive at least a user-input datuminto the input field, wherein the user-input datum describes data forselecting a preferred attribute of resource transfer data associated thefirst transfer datum; and display the instruction set includingdisplaying the first transfer datum based on the user-input datum. 12.The method of claim 11, wherein generating the interface query datastructure further comprises: retrieving, by the computing device, datadescribing attributes of the user from a database communicativelyconnected to the computing device; and generating the interface querydata structure based on the data describing attributes of the user. 13.The method of claim 11, wherein generating the target datum furthercomprises: retrieving, by the computing device, data describing currentpreferences of the client device between a minimum value and a maximumvalue from a database communicatively connected to the computing device;and generating, by the computing device, the interface query datastructure based on the data describing current preferences of the clientdevice.
 14. The method of claim 11, wherein generating the instructionset further comprises: classifying, by the computing device, at leastthe first transfer datum and the second transfer datum to the targetdatum; ranking, by the computing device, the first transfer datum andthe second transfer datum to the target datum; and adjusting, by thecomputing device, a threshold datum for triggering resource transferfrom the client device to the user based on the user-input datum. 15.The method of claim 11, wherein generating the instruction set furthercomprises: determining, by the computing device, a threshold datum byclassifying the pattern that is representative of client interactionwith the user to the user datum.
 16. The method of claim 15, whereingenerating the instruction set further comprises: adjusting, by thecomputing device, the pattern that is representative of clientinteraction with the user based on the threshold datum.
 17. The methodof claim 11, wherein generating the instruction set further comprises:classifying the client datum to one or more of the plurality ofcategories based on the pattern that is representative of clientinteraction with the user.
 18. The method of claim 11, furtherconfigured to evaluate the user-input datum comprising: classifying, bythe computing device, one or more new instances of the user-input datumwith at least the first transfer datum; generating, by the computingdevice, a consecutive transfer datum based on the classification; anddisplaying, by the computing device, the first transfer datum and atleast the consecutive transfer datum hierarchically based on theclassification of the consecutive transfer datum to one or more newinstances of the user-input datum.
 19. The method of claim 11, whereinclassifying the client datum and the user datum to a plurality ofcategories further comprises: aggregating, by the computing device, atleast the first transfer datum based on the classification; and furtherclassifying, by the computing device, aggregated transfer data to datadescribing the pattern that is representative of client interaction withthe user.
 20. The method of claim 11, wherein the interface query datastructure further configures the remote display device to: provide anarticulated graphical display including multiple regions organized in atree structure format, wherein each region provides one or moreinstances of point of interaction between the user and the remotedisplay device.